@inproceedings {27248,
	title = {Assessing Interactive Gaming Quality of Experience Using a Crowdsourcing Approach},
	journal = {2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)},
	year = {2020},
	publisher = { IEEE},
	abstract = {Traditionally, the Quality of Experience (QoE) is assessed in a controlled laboratory environment where participants give their opinion about the perceived quality of a stimulus on a standardized rating scale. Recently, the usage of crowdsourcing micro-task platforms for assessing the media quality is increasing. The crowdsourcing platforms provide access to a pool of geographically distributed, and demographically diverse group of workers who participate in the experiment in their own working environment and using their own hardware. The main challenge in crowdsourcing QoE tests is to control the effect of interfering influencing factors such as a user{\textquoteright}s environment and device on the subjective ratings. While in the past, the crowdsourcing approach was frequently used for speech and video quality assessment, research on a quality assessment for gaming services is rare. In this paper, we present a method to measure gaming QoE under typically considered system influence factors including delay, packet loss, and framerates as well as different game designs. The factors are artificially manipulated due to controlled changes in the implementation of games. The results of a total of five studies using a developed evaluation method based on a combination of the ITU-T Rec. P.809 on subjective evaluation methods for gaming quality and the ITU-T Rec. P.808 on subjective evaluation of speech quality with a crowdsourcing approach will be discussed. To evaluate the reliability and validity of results collected using this method, we finally compare subjective ratings regarding the effect of network delay on gaming QoE gathered from interactive crowdsourcing tests with those from equivalent laboratory experiments.},
	doi = {10.1109/QoMEX48832.2020.9123122},
	url = {https://ieeexplore.ieee.org/abstract/document/9123122},
	author = {Schmidt, Steven and Naderi, Babak and Saeed Shafiee {Sabet} and Zadtootaghaj, Saman},
	editor = {Moller, Sebastian}
}
@inproceedings {27247,
	title = {Delay Sensitivity Classification of Cloud Gaming Content},
	journal = {International Workshop on Immersive Mixed and Virtual Environment Systems (MMVE{\textquoteright}20)},
	year = {2020},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {Cloud Gaming is an emerging service that catches growing interest in the research community as well as industry. Cloud Gaming require a highly reliable and low latency network to achieve a satisfying Quality of Experience (QoE) for its users. Using a cloud gaming service with high latency would harm the interaction of the user with the game, leading to a decrease in playing performance and, thus players frustrations. However, the negative effect of delay on gaming QoE depends strongly on the game content. At a certain level of delay, a slow-paced card game is typically not as delay sensitive as a shooting game. For optimal resource allocation and quality estimation, it is highly important for cloud providers, game developers, and network planners to consider the impact of the game content. This paper contributes to a better understanding of the delay impact on QoE for cloud gaming applications by identifying game characteristics influencing the delay perception of the users. In addition, an expert evaluation methodology to quantify these characteristics as well as a delay sensitivity classification based on a decision tree are presented. The results indicated an excellent level of agreement, which demonstrates the reliability of the proposed method. Additionally, the decision tree reached an accuracy of 90\% on determining the delay sensitivity classes which were derived from a large dataset of subjective input quality ratings during a series of experiments.},
	isbn = {9781450379472},
	doi = {10.1145/3386293.3397116},
	url = {https://dl.acm.org/doi/abs/10.1145/3386293.3397116},
	author = {Saeed Shafiee {Sabet} and Schmidt, Steven and Zadtootaghaj, Saman},
	editor = {Griwodz, Carsten and Moller, Sebastian}
}
@inproceedings {27249,
	title = {A Latency Compensation Technique Based on Game Characteristics to Mitigate the Influence of Delay on Cloud Gaming Quality of Experience},
	journal = {ACM Multimedia Systems Conference 2020 (MMSys 2020)},
	year = {2020},
	pages = {15-25},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Cloud Gaming (CG) is an immersive multimedia service that promises many benefits. In CG, the games are rendered in a cloud server, and the resulted scenes are streamed as a video sequence to the client. Using CG users are not forced to update their gaming hardware frequently, and available games can be played on any operating system or suitable device. However, cloud gaming requires a reliable and low-latency network, which makes it a very challenging service. Transmission latency strongly affects the playability of a cloud game and consequently reduces the users{\textquoteright} Quality of Experience (QoE). In this paper, we propose a latency compensation technique using game adaptation that mitigates the influence of delay on QoE. This technique uses five game characteristics for the adaptation. These characteristics, in addition to an Aim-assistance technique, were implemented in four games for evaluation. A subjective study using 194 participants was conducted using a crowdsourcing approach. The results showed that the majority of the proposed adaptation techniques lead to significant improvements in the cloud gaming QoE.},
	doi = {10.1145/3339825.3391855},
	url = {https://dl.acm.org/doi/abs/10.1145/3339825.3391855},
	author = {Saeed Shafiee {Sabet} and Schmidt, Steven and Zadtootaghaj, Saman and Griwodz, Carsten and Moller, Sebastian}
}
@inproceedings {27250,
	title = {Quality Estimation Models for Gaming Video Streaming Services Using Perceptual Video Quality Dimensions},
	journal = {ACM Multimedia Systems Conference 2020 (MMSys 2020)},
	year = {2020},
	publisher = {Association for Computing Machinery (ACM)},
	address = {New York, NY, USA},
	abstract = {The gaming industry is one of the largest digital markets for decades and is steady developing as evident by new emerging gaming services such as gaming video streaming, online gaming, and cloud gaming. While the market is rapidly growing, the quality of these services depends strongly on network characteristics as well as resource management. With the advancement of encoding technologies such as hardware accelerated engines, fast encoding is possible for delay sensitive applications such as cloud gaming. Therefore, already existing video quality models do not offer a good performance for cloud gaming applications. Thus, in this paper, we provide a gaming video quality dataset that considers hardware accelerated engines for video compression using the H.264 standard. In addition, we investigate the performance of signal-based and parametric video quality models on the new gaming video dataset. Finally, we build two novel parametric-based models, a planning and a monitoring model, for gaming quality estimation. Both models are based on perceptual video quality dimensions and can be used to optimize the resource allocation of gaming video streaming services.},
	doi = {10.1145/3339825.3391872},
	url = {https://dl.acm.org/doi/abs/10.1145/3339825.3391872},
	author = {Zadtootaghaj, Saman and Schmidt, Steven and Saeed Shafiee {Sabet}},
	editor = {Griwodz, Carsten and Moller, Sebastian}
}
@inproceedings {27251,
	title = {Towards the Impact of Gamers Strategy and User Inputs on the Delay Sensitivity of Cloud Games},
	journal = {2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)},
	year = {2020},
	month = {23 June 2020},
	publisher = { IEEE},
	abstract = {Cloud Gaming is an emerging service that is considered by many as the future of the gaming industry. This service requires a highly reliable network with low latency and high bandwidth. If these requirements are not satisfied, cloud gaming services cannot create a good Quality of Experience (QoE) for its users. However, gaming QoE can vary significantly among different game scenarios and users. For an optimal resource allocation and quality estimation, it is highly important for cloud providers, game developers, and network planners to consider the influence of the game content and gamers. This paper presents the result of a subjective study that investigated the impact of different player strategies and user inputs on their perceived delay. The results indicated that the user input characteristics vary among the games but stays the same between different users and different strategies. In addition to the users{\textquoteright} inputs, the input quality and the overall gaming experience of the users were also investigated, and results did not show any main effect of user strategy on the delay sensitivity of the games.},
	isbn = {978-1-7281-5965-2},
	doi = {10.1109/QoMEX48832.2020.9123138},
	url = {https://ieeexplore.ieee.org/abstract/document/9123138},
	author = {Saeed Shafiee {Sabet}},
	editor = {Schmidt, Steven and Zadtootaghaj, Saman and Griwodz, Carsten and Moller, Sebastian}
}
